My PhD was published! My thesis is a simulation study of Resonant Tunneling Diodes (RTDs) performed with the Non-equilibrium Green's Function (NEGF) formalism. Specifically, it focusses on device variation in RTDs, and how this allows RTDs to compose Physical Unclonable Function (PUFs) which can uniquely identify devices they are placed on, in order to fight counterfeiting. As part of this, I improved the device variation capabilities of Nano-electronic Simulation Software (NESS).
"Sensitivity of resonant tunneling diodes to barrier variation and quantum well variation: A NEGF study" was published. For this paper, I studied the impact of varying barrier and Quantum Well (QW) thicknesses on RTD current-voltage (IV) characteristics. I also studied the impact of Interface Roughness (IR) at different parts of the RTD, and compared with the effects of thickness, concluding that IR along barriers leads to thicker effect barrier and IR along the QW leads to thinner effect QWs.
"Interface roughness in Resonant Tunnelling Diodes for physically unclonable functions" was published. This accompanies my EuroSOI-ULIS 2024 conference presentation. This was on how IR along heterostructure interfaces in RTD barriers, such as between a GaAs body and AlGaAs barriers, leads to random variation of the resulting IV characteristics. This random variation of the output can then be used to encode information, which means multiple RTDs can together form a PUF in order to uniquely identify whichever device they are placed on.
"Diffusion-Based Machine Learning Method for Accelerating Quantum Transport Simulations in Nanowire Transistors" was presented for SISPAD 2024 by my colleague Preslav Aleksandrov. I'm pleased to work with him on this study, where a Machine Learning (ML) diffusion model was used to accelerate NEGF simulations. NEGF is accurate and captures quantum effects such quantum tunneling, yet it can take a long time, with multiple iterations of NEGF needed in order to pass the conditions set for a successful simulation. The purpose of this paper was to explore the use of Diffusion-based ML models to accelerate this time consuming process. In this paper UNet architecture was demonstrated being trained on simulation data, in order to provide a better simulation starting point for NEGF simulations, accelerating NESS by up to 60%.
"Impact of interface roughness correlation on resonant tunnelling diode variation" was published. This study improved the IR generation in NESS to have two correlation lengths, and showed that by having IR along both dimensions in a plane that the variation in the resulting IV characteristics increases. It was also found that increasing correlation length leads to greater IV variation.
"The study of electron mobility on ultra-scaled silicon nanosheet FET" was published. I'm glad to be able to help Tongfui Liu et al with this study. The impact of phonon and surface roughness scattering on the electron mobility of n-type silicon nanosheet field-effect transistors (FET) was studied.
My first paper, "Analysis of Random Discrete Dopants Embedded Nanowire Resonant Tunnelling Diodes for Generation of Physically Unclonable Functions", was published today! I studied the impact of Random Discrete Dopants (RDDs) on RTD, and showed that the resulting variation in IV characteristics allows the encoding of information. Hence, multiple RDD doped RDDs can form PUFs which uniquely identify devices they are placed on.
I'm grateful to present my research in sunny Athens, and take to part in this engaging conference. To carry out this research, I expanded the roughness in NESS from surface roughness to IR as well. This presentation explained how IR along heterostructure interface in RTD barriers, such as between a GaAs body and AlGaAs barriers, lead to random variation of the resulting current-voltage characteristics in simulated RTDs. This random variation of the output can then be used to encode information, which means multiple RTDs can together form a PUF to uniquely identify the device which they are placed on.
I'm pleased to help the University of Glasgow host the 9th SINANO Modelling and Simulation Summer School.
Nearly a year since my Masters ended, I'm glad to say that I've graduated from Lancaster University. While it was interrupted by the pandemic, it was an invaluable time, including a year spent studying abroad at Nanyang Technological University (NTU), Singapore.
I'm looking forward to a new chapter of my research career, starting my PhD at the University of Glasgow.
I'm glad to complete my Masters in Theoretical Physics at Lancaster University with a First Class Honours.
I presented for my Masters project at Lancaster University's 2021 Undergraduate Research Conference. For this project, I simulated excitonic complexes, composed of electrons and holes, within a type 2 (which means one type of charge carrier is confined, which in this case is holes) quantum dot. In particular, I studied the binding energies of excitonic complexes, which affects the frequency and thus colour of photons emitted upon decay of these complexes. This impact on the optoelectronic of quantum dots extends to commercial applications such as solar panels and QLED TVs.
I'm looking forward to starting my Masters in Theoretical Physics at Lancaster University.